An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

Abstract

Nonlinear regression analysis is an important statistical method widely used in many fields of science to model the complex relationships between variables. Therefore, many studies have been conducted to estimate the parameters of nonlinear regression models using various iterative techniques. In this study, an efficient hybrid algorithm, namely PSONM, by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm is proposed to obtain parameter estimates of nonlinear regression models. To show the performance of the proposed hybrid algorithm, 20 nonlinear regression tasks with various levels of difficulty, and real data sets in the agriculture field have been tested. The experimental results indicated that the suggested hybrid algorithm provides accurate estimates, and its performance is much superior to those of NM and PSO algorithms.